Practical machine learning for disease diagnosis

Huw D. Summers

doi:10.1016/j.crmeth.2021.100103

Practical machine learning for disease diagnosis

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification.

Original language	English (US)
Article number	100103
Journal	Cell Reports Methods
Volume	1
Issue number	6
DOIs	https://doi.org/10.1016/j.crmeth.2021.100103
State	Published - Oct 25 2021

ASJC Scopus subject areas

Biotechnology
Biochemistry
Biochemistry, Genetics and Molecular Biology (miscellaneous)
Genetics
Radiology Nuclear Medicine and imaging
Computer Science Applications

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10.1016/j.crmeth.2021.100103

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title = "Practical machine learning for disease diagnosis",

abstract = "Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification.",

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note = "Funding Information: The author benefitted from stimulating discussions with Paul Rees on machine learning and cell classification. The author declares no competing interests. Publisher Copyright: {\textcopyright} 2021 The Author",

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AB - Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification.

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Practical machine learning for disease diagnosis

Abstract

ASJC Scopus subject areas

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